Seq2Seq model produces repeating words
The trained model predicts the probability of a given sequence of tokens. Whatever NLP task you are doing, you usually want to get a high-probability sample from that probability distribution. This sampling task could be quite non-trivial.
What you are seeing is most likely the result of a greedy sampling - the most probable next word is chosen from the probability distribution. This quite often leads to infinite repletion loops as you experience.
Simplest way to solve the repetition problem is to actually randomly sample from the probability distribution of the next word - the distribution is usually augmented with so-called temperature parameter.
Finally, there's beam search - you just perform a tree search for a best N-gram sample in the future. That's the most sophisticated, and the most computationally hard one.